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The main objective of this paper is to explore deep decarbonization pathways for the Canadian energy sector that would allow Canada to participate in global mitigation efforts to keep global mean surface temperatures from increasing by more than 2 °C by 2100. Our approach consists in deriving minimum cost solutions for achieving progressive emission reductions up to 2050 using the North American TIMES Energy Model (NATEM), a detailed multi-regional and integrated optimization energy model. With this model, we analyze a baseline and two 60% reduction scenarios of combustion related emissions by 2050 from 1990 levels, with different assumptions regarding projected demands for energy services and availability of technology options for carbon mitigation. The first reduction scenario includes only well-known technologies while the second one considers additional disruptive technologies, which are known but are not fully developed commercially. Results show that three fundamental transformations need to occur simultaneously in order to achieve ambitious GHG emission reduction targets: electrification of end-use sectors, decarbonization of electricity generating supply, and efficiency improvements. In particular, our results show that electricity represents between 52% and 57% of final energy consumption by 2050, electricity generating supply achieves nearly complete decarbonization by 2025 and final energy consumption decreases by 20% relative to the baseline by 2050. [ABSTRACT FROM AUTHOR]

The Lagrange Multiplier (LM) test is one of the principal tools to detect ARCH and GARCH effects in financial data analysis. However, when the underlying data are non-normal, which is often the case in practice, the asymptotic LM test, based on the χ2-approximation of critical values, is known to perform poorly, particularly for small and moderate sample sizes. In this paper we propose to employ two re-sampling techniques to find critical values of the LM test, namely permutation and bootstrap. We derive the properties of exactness and asymptotically correctness for the permutation and bootstrap LM tests, respectively. Our numerical studies indicate that the proposed re-sampled algorithms significantly improve size and power of the LM test in both skewed and heavy-tailed processes. We also illustrate our new approaches with an application to the analysis of the Euro/USD currency exchange rates and the German stock index. The Canadian Journal of Statistics 40: 405-426; 2012 © 2012 Statistical Society of Canada [ABSTRACT FROM AUTHOR]

In this paper, an integrated simulation—optimization approach is proposed for annual planning of power restoration workforce related to an electricity distribution company in a province of Canada. Internal and external workforces are employed to perform maintenance actions and restore power after interruptions throughout the province. According to the electricity distribution network, the province is divided into a number of work locations (WL), each having local crews to perform maintenance actions and fix power interruptions. However, determining the size of the crew in each WL over the year is challenging because of high fluctuation in interruption frequency and consequently in projected demand during the year. The frequency of interruptions is affected by various factors such as geographical location, time calendar, and particularly weather conditions. The objective is to determine the optimal combination of internal and external workforce over the year to cover the interruptions across the province with minimum cost and minimum customer interruption duration. [ABSTRACT FROM PUBLISHER]

In this study, an inexact fuzzy chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is proposed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing inexact two-stage programming and mixed-integer linear programming techniques by incorporating uncertainties expressed as multiple uncertainties of intervals and dual probability distributions within a general optimization framework. The developed method can provide an effective linkage between the predefined environmental policies and the associated economic implications. Four special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it provides a linkage to predefined policies that have to be respected when a modeling effort is undertaken; secondly, it is useful for tackling uncertainties presented as intervals, probabilities, fuzzy sets and their incorporation; thirdly, it facilitates dynamic analysis for decisions of facility-expansion planning and waste-flow allocation within a multi-facility, multi-period, multi-level, and multi-option context; fourthly, the penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised solid waste-generation rates are violated. In a companion paper, the developed method is applied to a real case for the long-term planning of waste management in the City of Regina, Canada. [Copyright &y& Elsevier]

This paper examines the problems of routing freight traffic, scheduling train services and allocating classification work between yards on a rail network. It describes a general optimization model which takes into account the interactions between these activities and develops global strategies for their management over a medium term planning horizon. A heuristic algorithm developed to solve this nonlinear, mixed-integer multicommodity flow problem is also presented with some computational results based on an example at the Canadian National Railroads. [ABSTRACT FROM AUTHOR]